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WTF is a knowledge graph and why does it matter to AI and your small business?

Written by Brian Birch | Dec 16, 2025 1:32:38 PM

By Dimitri Goudis and Brian Birch

Summary: Knowledge Graphs are a different way to store information, making that data easier to read for AI-based tools. This can be a game-changer for small organizations and businesses, so its a good idea to begin by understanding what they are and how they work.

Defining a Knowledge Graph

A Knowledge Graph (KG) is a data model that structures knowledge by linking things called “entities” together and defining the relationships, also called “edges” between them. Unlike a traditional database that stores data in isolated tables, a KG captures the context and meaning of the data.

The core building blocks of a Knowledge Graph are:

  • Entities (Nodes): These are the “things”, like a customer, a product, a skill, or an event.
  • Relationships (Edges): These are the connections between them. For example, “works for,” “is a part of,” or “purchased.”
  • Properties: These are additional attributes on the nodes (e.g., a customer’s location) and the edges (e.g., the date of a purchase).

All of this together creates what we call the ontology/schema of a graph database. This gives it visible structure and meaning, and is also what makes it readable by AI. It helps the “machine” reason with information, just like humans do, in complex ways.

Let’s dig into that a little bit more.

How Knowledge graphs enhance AI usage

Generative AI models, especially Large Language Models (LLMs), are excellent at language generation, but they can get confused or simply make things up (called hallucinating). They need as much context as possible, or they try to fill in the gaps with other information they may have been trained on.

Knowledge Graphs provide the context and factual grounding needed to make generative AI more accurate, relevant, and trustworthy.

And the best news? They can be used to help both humans and AI visually and contextually understand complex data faster and more efficiently. Let’s examine that more for real-world settings.

Applications for small nonprofits and businesses

Knowledge Graphs are scalable and can be tailored to an organization’s specific needs, even for smaller teams:

Example 1: Knowledge management

  • The problem: Documents, FAQs, product specs, and expert contacts are scattered across file systems, emails, and shared drives.
  • Knowledge graph solution: Create an internal “Enterprise Knowledge Graph” that connects all these disparate pieces of information.

Example: An employee can ask a natural language question (e.g., “Who worked on this project last year and what were the challenges?”). The graph finds the connections between people, projects, and documents to provide a precise, consolidated answer.

Example 2: Customer service chatbots

  • The problem: Basic chatbots can only answer simple, pre-programmed FAQs. They fail when the question requires combining information or comparison to similar issues.
  • Knowledge graph solution: Connect the dots for the AI-powered chatbot, linking key variables (for example, common tech issues customers face, existing bugs, etc.).
Example: A customer asks, “I keep getting this error when I click on the login link” The KG links customers’ experience and broadens it to similar experiences and solutions that have already occurred, helping them get the answers they need without waiting for a live staff person or submitting a ticket.

Example 3: Event speaker/topic matching and gap analysis

  • The Problem: Building a balanced conference agenda is hard, and matching speaker proposals to event themes is often manual and subjective. Organizers may overlook perfect speakers because their proposals didn’t use the exact keywords for a particular track, or they may unknowingly create content overlap (too many talks on one topic) or gaps (no submissions on a key theme).
  • Knowledge Graph Solution: Map content and identify synergies, then build a minimal graph that connects the information using structured content (like abstracts and speaker bios). This creates a flexible, objective view of the entire content landscape.

A Knowledge Graph essentially turns complex, fragmented information into a unified area that can be consumed quickly and easily by AI to help solve problems.

Harrier Takeaway

For now, familiarize yourself with the concept of a knowledge graph and do some research about simple use cases for them, and work with your internal staff and favorite GenAI tool (ChatGPT, Gemini, etc.) to explore how you may be able to apply them in the future.

Sources/additional information
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